In the art of telecommunications, it is desirable to estimate the average noise power contained in a received signal. Typically, the received signal is a frequency division multiplexed composite signal containing several distinct signals. It is important to be able to estimate the noise power without a priori knowledge of the number, nature, carrier-frequency, bandwidth or amplitudes of the distant signals.
In the art, noise is considered an additive phenomenon. It distorts a signal by adding random time-varying values to the amplitude of the non-noise component of the signal. It is thus of great importance to develop a radio receiver that is robust to the noise phenomenon.
The general problem of estimating the variance of the additive noise in a communication signal composed of many narrowband frequency division multiplex signals is important in many radio communication systems. It allows setting the detectors threshold to its maximum sensitivity while limiting the probability of a false alarm to a value less than or equal to a desired level. The detector decides that a signal is present when the energy of a signal, over a period of time, exceeds a threshold value. The detected signals can then be passed to a receiver or to measurement equipment, for demodulation or to acquire more information about the signal.
Currently there are several techniques that are used in this pursuit, but all come with sacrifices. One such technique is to reserve a portion of the bandwidth of the composite signal to carry a “noise channel”. Thus by measuring the power in this reserved band, an estimate of the noise power in the other channels can be obtained. It is common though for these “noise channels” to be difficult to identify as their placement in the spectrum of the composite signal is not governed by any global convention which leads to different channel assignments based on geographical location of the measurement equipment. Other errors are introduced when the “noise channel” is affected by the signals in adjacent channels. This results in a biased estimation of the noise floor. If the channel assignment is unknown, it may not be possible to identify the “noise channel” and determine the noise floor power level. In addition to these problems the noise floor tends to fluctuate with environmental and operation conditions.
In the art, it is common practice to set a noise threshold value, determined in large part by the estimated noise floor, whereby a signal is considered present if and only if it is above the noise threshold.
The traditional approach is to set a threshold value depending on the user needs. When high sensitivity is required, the threshold is set to a value close to the user observed noise floor level. When only strong signals are of interest, the threshold is set to a high value relative to the observed noise floor level. This approach is highly subjective and may result in being too aggressive or too conservative for the actually measured signal environment. The threshold is typically set manually in a communications system. This is not feasible when fast response to dynamically changing conditions is required. Very few automated techniques have been studied to address the general problem of rapidly and dynamically estimating the noise floor level or the signal-to-noise ratio. One typical method is to isolate a channel and to assign this channel as a noise only channel for estimation purposes. This method is difficult to apply in many new digital communications systems where the resources are often utilized close to their capacity. It is also a problem for dynamic signal environments based on multiple access. Another typical method is to assume that the composite wideband signal non-noise subspace dimension is known. This is again difficult to realize in practical situations with unknown signals where robustness is a key feature. On another front, a technique based on morphological binary image processing operators (similar to rank-order filtering) as described in “Automatic Noise Floor Spectrum Estimation in the Presence of Signals”, M. J. Ready, M. L. Downey, and L. J. Corbalis, Proceedings of the 31st Asilomar Conference on Signals, Systems and Computers, vol. 1, pp 877-881, November 1997, processes a binary image of the wideband received power spectrum of a composite signal has been proposed. Thus, the approach does not process the time samples or frequency domain transformed data directly, but the instead it processes an image of the spectrum. This involves two dimensional signal processing that is usually complex. Furthermore, practical performances results have yet to be quantified with such an approach. In general, techniques based on textbook results tend to assume several conditions that are not truly present in many bands of the radio spectrum.
The computational complexity of a method is a limiting factor of the method's applicability. Typically, a spectrum monitoring receiver cannot observe the full spectrum it is allotted, thus it divides the spectrum into smaller blocks that can be scanned, each of these blocks possibly contains a plurality of signals. Each block is received sequentially, an estimate of the noise floor is arrived at, and then the receiver shifts its frequency to receive another block. This cycling through the allotted bandwidth must be done rapidly to ensure that all messages being transmitted in the different parts of the allotted spectrum can be received. If the method to derive a noise floor estimate is too complex then it either cannot keep pace with the shifting of the block or it causes the shifting of the blocks to slow down, in the event of either result the method ceases to be useful.
Thus, it would be desirable to design a system that resolves the problems that the above mentioned methods have with narrowband signals of unknown nature, as described in “Noise Floor Estimation for Wide-Band FFT Filter Banks”. It would also be desired for a new system to have fully digital characteristics so as to permit real-time signal performance, without requiring the dedication of a reserved frequency band for use as a noise channel.